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Open AccessArticle

Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training

1
BISITE Research Group, University of Salamanca, 37008 Salamanca, Spain
2
Department of Applied Mathematics, Institute of Fundamental Physics and Mathematics, University of Salamanca, 37008 Salamanca, Spain
3
Escuela Superior de Ingeniería y Tecnología, Universidad Internacional de La Rioja, Av. La Paz 137, 26006 Logroño, Spain
*
Author to whom correspondence should be addressed.
Academic Editors: Zeev Volkovich, Oleg Granichin, Dvora Toledano-Kitai and Paolo Crippa
Mathematics 2021, 9(4), 421; https://doi.org/10.3390/math9040421
Received: 30 December 2020 / Revised: 1 February 2021 / Accepted: 17 February 2021 / Published: 21 February 2021
(This article belongs to the Special Issue Machine Learning and Data Mining in Pattern Recognition)
Evaluating web traffic on a web server is highly critical for web service providers since, without a proper demand forecast, customers could have lengthy waiting times and abandon that website. However, this is a challenging task since it requires making reliable predictions based on the arbitrary nature of human behavior. We introduce an architecture that collects source data and in a supervised way performs the forecasting of the time series of the page views. Based on the Wikipedia page views dataset proposed in a competition by Kaggle in 2017, we created an updated version of it for the years 2018–2020. This dataset is processed and the features and hidden patterns in data are obtained for later designing an advanced version of a recurrent neural network called Long Short-Term Memory. This AI model is distributed training, according to the paradigm called data parallelism and using the Downpour training strategy. Predictions made for the seven dominant languages in the dataset are accurate with loss function and measurement error in reasonable ranges. Despite the fact that the analyzed time series have fairly bad patterns of seasonality and trend, the predictions have been quite good, evidencing that an analysis of the hidden patterns and the features extraction before the design of the AI model enhances the model accuracy. In addition, the improvement of the accuracy of the model with the distributed training is remarkable. Since the task of predicting web traffic in as precise quantities as possible requires large datasets, we designed a forecasting system to be accurate despite having limited data in the dataset. We tested the proposed model on the new Wikipedia page views dataset we created and obtained a highly accurate prediction; actually, the mean absolute error of predictions regarding the original one on average is below 30. This represents a significant step forward in the field of time series prediction for web traffic forecasting. View Full-Text
Keywords: web traffic forecast; time series forecast; LSTM; parameter averaging; Downpour strategy; pattern extraction web traffic forecast; time series forecast; LSTM; parameter averaging; Downpour strategy; pattern extraction
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MDPI and ACS Style

Casado-Vara, R.; Martin del Rey, A.; Pérez-Palau, D.; de-la-Fuente-Valentín, L.; Corchado, J.M. Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training. Mathematics 2021, 9, 421. https://doi.org/10.3390/math9040421

AMA Style

Casado-Vara R, Martin del Rey A, Pérez-Palau D, de-la-Fuente-Valentín L, Corchado JM. Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training. Mathematics. 2021; 9(4):421. https://doi.org/10.3390/math9040421

Chicago/Turabian Style

Casado-Vara, Roberto; Martin del Rey, Angel; Pérez-Palau, Daniel; de-la-Fuente-Valentín, Luis; Corchado, Juan M. 2021. "Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training" Mathematics 9, no. 4: 421. https://doi.org/10.3390/math9040421

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